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Liu Q, Si H, Li Y, Zhou W, Yu J, Bian Y, Wang C. Development and validation of a risk scoring tool for predicting incident reversible cognitive frailty among community-dwelling older adults: A prospective cohort study. Geriatr Gerontol Int 2024. [PMID: 39048538 DOI: 10.1111/ggi.14942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 06/19/2024] [Accepted: 07/04/2024] [Indexed: 07/27/2024]
Abstract
AIM Reversible cognitive frailty (RCF) is an ideal target to prevent asymptomatic cognitive impairment and dependency. This study aimed to develop and validate prediction models for incident RCF. METHODS A total of 1230 older adults aged ≥60 years from China Health and Retirement Longitudinal Study 2011-2013 survey were included as the training set. The modified Poisson regression and three machine learning algorithms including eXtreme Gradient Boosting, support vector machine and random forest were used to develop prediction models. All models were evaluated internally with fivefold cross-validation, and evaluated externally using a temporal validation method through the China Health and Retirement Longitudinal Study 2013-2015 survey. RESULTS The incidence of RCF was 27.4% in the training set and 27.5% in the external validation set. A total of 13 important predictors were selected to develop the model, including age, education, contact with their children, medical insurance, vision impairment, heart diseases, medication types, self-rated health, pain locations, loneliness, self-medication, night-time sleep and having running water. All models showed acceptable or approximately acceptable discrimination (AUC 0.683-0.809) for the training set, but fair discrimination (AUC 0.568-0.666) for the internal and external validation. For calibration, only modified Poisson regression and eXtreme Gradient Boosting were acceptable in the training set. All models had acceptable overall prediction performance and clinical usefulness. Older adults were divided into three groups by the risk scoring tool constructed based on modified Poisson regression: low risk (≤24), median risk (24-29) and high risk (>29). CONCLUSIONS This risk tool could assist healthcare providers to predict incident RCF among older adults in the next 2 years, facilitating early identification of a high-risk population of RCF. Geriatr Gerontol Int 2024; ••: ••-••.
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Affiliation(s)
- Qinqin Liu
- School of Nursing, Peking University, Beijing, China
| | - Huaxin Si
- School of Public Health, Peking University, Beijing, China
| | - Yanyan Li
- School of Nursing, Peking University, Beijing, China
| | - Wendie Zhou
- School of Nursing, Peking University, Beijing, China
| | - Jiaqi Yu
- School of Nursing, Peking University, Beijing, China
| | - Yanhui Bian
- School of Nursing, Peking University, Beijing, China
| | - Cuili Wang
- School of Nursing, Peking University, Beijing, China
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Liu Q, Yang L, Shi Z, Yu J, Si H, Jin Y, Bian Y, Li Y, Ji L, Qiao X, Wang W, Liu H, Zhang M, Wang C. Development and validation of a preliminary clinical support system for measuring the probability of incident 2-year (pre)frailty among community-dwelling older adults: A prospective cohort study. Int J Med Inform 2023; 177:105138. [PMID: 37516037 DOI: 10.1016/j.ijmedinf.2023.105138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 06/18/2023] [Accepted: 06/26/2023] [Indexed: 07/31/2023]
Abstract
OBJECTIVE To develop the wed-based system for predicting risk of (pre)frailty among community-dwelling older adults. MATERIALS AND METHODS (Pre)frailty was determined by physical frailty phenotype scale. A total of 2802 robust older adults aged ≥60 years from the China Health and Retirement Longitudinal Study (CHARLS) 2013-2015 survey were randomly assigned to derivation or internal validation cohort at a ratio of 8:2. Logistic regression, Random Forest, Support Vector Machine and eXtreme Gradient Boosting (XGBoost) were used to construct (pre)frailty prediction models. The Grid search and 5-fold cross validation were combined to find the optimal parameters. All models were evaluated externally using the temporal validation method via the CHARLS 2011-2013 survey. The (pre)frailty predictive system was web-based and built upon representational state transfer application program interfaces. RESULTS The incidence of (pre)frailty was 34.2 % in derivation cohort, 34.8 % in internal validation cohort, and 32.4 % in external validation cohort. The XGBoost model achieved better prediction performance in derivation and internal validation cohorts, and all models had similar performance in external validation cohort. For internal validation cohort, XGBoost model showed acceptable discrimination (AUC: 0.701, 95 % CI: [0.655-0.746]), calibration (p-value of Hosmer-Lemeshow test > 0.05; good agreement on calibration plot), overall performance (Brier score: 0.200), and clinical usefulness (decision curve analysis: more net benefit than default strategies within the threshold of 0.15-0.80). The top 3 of 14 important predictors generally available in community were age, waist circumference and cognitive function. We embedded XGBoost model into the server and this (pre)frailty predictive system is accessible at http://www.frailtyprediction.com.cn. A nomogram was also conducted to enhance the practical use. CONCLUSIONS A user-friendly web-based system was developed with good performance to assist healthcare providers to measure the probability of being (pre)frail among community-dwelling older adults in the next two years, facilitating the early identification of high-risk population of (pre)frailty. Further research is needed to validate this preliminary system across more controlled cohorts.
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Affiliation(s)
- Qinqin Liu
- School of Nursing, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China
| | - Liming Yang
- School of Computer Science, Peking University, Beijing 100871, China
| | - Zhuming Shi
- School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
| | - Jiaqi Yu
- School of Nursing, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China
| | - Huaxin Si
- School of Nursing, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China
| | - Yaru Jin
- School of Nursing, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China
| | - Yanhui Bian
- School of Nursing, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China
| | - Yanyan Li
- School of Nursing, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China
| | - Lili Ji
- School of Nursing, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China
| | - Xiaoxia Qiao
- School of Nursing, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China
| | - Wenyu Wang
- School of Nursing, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China
| | - Hongpeng Liu
- School of Nursing, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China
| | - Ming Zhang
- School of Computer Science, Peking University, Beijing 100871, China
| | - Cuili Wang
- School of Nursing, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China.
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Mennickent D, Rodríguez A, Opazo MC, Riedel CA, Castro E, Eriz-Salinas A, Appel-Rubio J, Aguayo C, Damiano AE, Guzmán-Gutiérrez E, Araya J. Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Front Endocrinol (Lausanne) 2023; 14:1130139. [PMID: 37274341 PMCID: PMC10235786 DOI: 10.3389/fendo.2023.1130139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/04/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology. Aim To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications. Methodology Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations. Current state ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used. Future challenges To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models. Conclusion The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.
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Affiliation(s)
- Daniela Mennickent
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
| | - Andrés Rodríguez
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
- Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad del Bío-Bío, Chillán, Chile
| | - Ma. Cecilia Opazo
- Instituto de Ciencias Naturales, Facultad de Medicina Veterinaria y Agronomía, Universidad de Las Américas, Santiago, Chile
- Millennium Institute on Immunology and Immunotherapy, Santiago, Chile
| | - Claudia A. Riedel
- Millennium Institute on Immunology and Immunotherapy, Santiago, Chile
- Departamento de Ciencias Biológicas, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago, Chile
| | - Erica Castro
- Departamento de Obstetricia y Puericultura, Facultad de Ciencias de la Salud, Universidad de Atacama, Copiapó, Chile
| | - Alma Eriz-Salinas
- Departamento de Obstetricia y Puericultura, Facultad de Medicina, Universidad de Concepción, Concepción, Chile
| | - Javiera Appel-Rubio
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
| | - Claudio Aguayo
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
| | - Alicia E. Damiano
- Cátedra de Biología Celular y Molecular, Departamento de Ciencias Biológicas, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires, Argentina
- Laboratorio de Biología de la Reproducción, Instituto de Fisiología y Biofísica Bernardo Houssay (IFIBIO-Houssay)- CONICET, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Enrique Guzmán-Gutiérrez
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
| | - Juan Araya
- Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
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Karpov OE, Pitsik EN, Kurkin SA, Maksimenko VA, Gusev AV, Shusharina NN, Hramov AE. Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5335. [PMID: 37047950 PMCID: PMC10094658 DOI: 10.3390/ijerph20075335] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/17/2023] [Accepted: 03/22/2023] [Indexed: 06/19/2023]
Abstract
Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices has shown great promise in enhancing the accuracy and efficiency of diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. This paper aims at the exploration of the AI-based medicine research using network approach and analysis of existing trends based on PubMed. Our findings are based on the results of PubMed search queries and analysis of the number of papers obtained by the different search queries. Our goal is to explore how are the AI-based methods used in healthcare research, which approaches and techniques are the most popular, and to discuss the potential reasoning behind the obtained results. Using analysis of the co-occurrence network constructed using VOSviewer software, we detected the main clusters of interest in AI-based healthcare research. Then, we proceeded with the thorough analysis of publication activity in various categories of medical AI research, including research on different AI-based methods applied to different types of medical data. We analyzed the results of query processing in the PubMed database over the past 5 years obtained via a specifically designed strategy for generating search queries based on the thorough selection of keywords from different categories of interest. We provide a comprehensive analysis of existing applications of AI-based methods to medical data of different modalities, including the context of various medical fields and specific diseases that carry the greatest danger to the human population.
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Affiliation(s)
- Oleg E. Karpov
- National Medical and Surgical Center Named after N. I. Pirogov, Ministry of Healthcare of the Russian Federation, 105203 Moscow, Russia
| | - Elena N. Pitsik
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
| | - Semen A. Kurkin
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
| | - Vladimir A. Maksimenko
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
| | - Alexander V. Gusev
- K-Skai LLC, 185031 Petrozavodsk, Russia
- Federal Research Institute for Health Organization and Informatics, 127254 Moscow, Russia
| | - Natali N. Shusharina
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
| | - Alexander E. Hramov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
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A Semi-Supervised Machine Learning Approach in Predicting High-Risk Pregnancies in the Philippines. Diagnostics (Basel) 2022; 12:diagnostics12112782. [PMID: 36428842 PMCID: PMC9689356 DOI: 10.3390/diagnostics12112782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/02/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022] Open
Abstract
Early risk tagging is crucial in maternal health, especially because it threatens both the mother and the long-term development of the baby. By tagging high-risk pregnancies, mothers would be given extra care before, during, and after pregnancies, thus reducing the risk of complications. In the Philippines, where the fertility rate is high, especially among the youth, awareness of risks can significantly contribute to the overall outcome of the pregnancy and, to an extent, the Maternal mortality rate. Although supervised machine learning models have ubiquity as predictors, there is a gap when data are weak or scarce. Using limited collected data from the municipality of Daraga in Albay, the study first compared multiple supervised machine learning algorithms to analyze and accurately predict high-risk pregnancies. Through hyperparameter tuning, supervised learning algorithms such as Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Multilayer Perceptron were evaluated by using 10-fold cross validation to obtain the best parameters with the best scores. The results show that Decision Tree bested other algorithms and attained a test score of 93.70%. To address the gap, a semi-supervised approach using a Self-Training model was applied to the modified Decision Tree, which was then used as the base estimator with a 30% unlabeled dataset and achieved a 97.01% accuracy rate which outweighs similar studies.
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Cohort Study Summary of the Effects of Carboprost Tromethamine Combined with Oxytocin on Infant Outcome, Postpartum Hemorrhage and Uterine Involution of Parturients Undergoing Cesarean Section. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2233138. [PMID: 36060654 PMCID: PMC9436546 DOI: 10.1155/2022/2233138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/20/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022]
Abstract
Background Carboprost tromethamine injection has a high safety factor in clinical application and has a good effect on uterine smooth muscle and vasoconstriction. Carboprost aminobutyriol combined with oxytocin may be beneficial to infant outcome and uterine involution after cesarean section. Objective To investigate the effects of carboprost tromethamine combined with oxytocin on infant outcome, postpartum hemorrhage, and uterine involution in parturients undergoing cesarean section. Methods A total of 120 parturients undergone cesarean section in our hospital from February 2019 to April 2021 were selected as the object of study. The parturients were randomly divided into control group (n = 60) and research group (n = 60). The control group was treated with oxytocin, and the research group was treated with carboprost aminobutyriol combined with oxytocin. The amount of maternal bleeding, uterine floor decline index, the end of lochia, poor rate of uterine involution, infant outcome, and the incidence of adverse drug reactions were compared between the two groups. Results The amount of bleeding in the research group was significantly lower than that in the control group (P < 0.05). The position of the last uterine floor and the index of uterine floor downward movement in the research group were significantly higher than those in the control group (P < 0.05). The disappearance time of bloody lochia and serous lochia in the research group was significantly shorter than that in the control group (P < 0.05). The end time of lochia in the research group was higher than that in the control group, and the rate of uterine involution in the research group was lower than that in the control group (P < 0.05). The neonatal weight and Apgar score in the research group were higher than those in the control group, and the hospitalization rate of neonatal ICU in the research group was significantly lower than that in the control group. The incidence of adverse reactions in the research group was significantly lower than that in the control group (P < 0.05). Conclusion Carboprost aminobutyriol combined with carbestatin can effectively prevent the occurrence of bleeding after cesarean section, improve uterine involution, and improve neonatal birth quality, which is worth popularizing.
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